AUTHOR=Hu Yong Mei , Huang Hua , Chen Yu Ting , Wang Wen Jin , Zhang Bai Hui 

TITLE=Correlation between systemic inflammatory response index and post-stroke epilepsy based on multiple logistic regression analysis

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1640796

DOI=10.3389/fneur.2025.1640796

ISSN=1664-2295

ABSTRACT=BackgroundPost-stroke epilepsy (PSE) is an important neurological complication affecting the prognosis of stroke patients. Recent studies have found that the systemic inflammatory response index (SIRI) is a new inflammatory marker, and its mechanism of association with PSE is not yet clear. The purpose of this study was to investigate the correlation between SIRI level and the occurrence of PSE.MethodsThe study retrospectively included 226 stroke patients admitted from July 2021 to October 2024. According to the occurrence of epilepsy, they were divided into PSE group (n = 57) and non-PSE group (n = 169). Multivariate Logistic regression analysis was used to evaluate the correlation strength between SIRI and PSE, and the Restricted Cubic Spline (RCS) model was used to explore the strong nonlinear relationship between SIRI and PSE. At the same time, the stratified analysis method was used to deeply explore the basic disease status and correct the following variables: (1) Demographic characteristics: social demographic indicators such as age, gender, BMI and education level were included; (2) lifestyle factors: including smoking status and drinking habits; (3) complications: clinical diagnosed diseases such as hypertension, diabetes, coronary heart disease and chronic obstructive pulmonary disease; (4) laboratory test parameters: including neutrophils, lymphocytes, monocytes and other blood cell classification counts, as well as biochemical indicators such as platelets, hemoglobin, total protein and total cholesterol.ResultsThe baseline SIRI level in the PSE group was significantly higher than that in the non-PSE group (3.43 ± 2.74 vs. 1.79 ± 1.40, p < 0.001). Stratified analysis showed that there was a significant interaction between SIRI and PSE in the subgroup with underlying diseases (p < 0.001). The RCS analysis also suggested that there was a nonlinear positive correlation between SIRI and PSE risk (p = 0.382), and the risk inflection point appeared when SIRI = 1.36.ConclusionThis study shows that elevated SIRI is associated with the occurrence of PSE, especially in stroke patients with underlying diseases. The results of this study provide a new reference of inflammatory biomarkers for early warning and hierarchical management of PSE.